标题:Intelligent scheduling with deep fusion of hardware-software energy-saving principles for greening stochastic nonlinear heterogeneous super-systems
作者:Wang, Jinglian; Gong, Bin; Liu, Hong; Li, Shaohui
作者机构:[Wang, Jinglian] Ludong Univ, Sch Informat & Elect Engn, Yantai, Peoples R China.; [Wang, Jinglian; Gong, Bin] Shandong Univ, Sch Comp Sci & Technol 更多
通讯作者:Wang, JL;Wang, JL;Wang, Jinglian
通讯作者地址:[Wang, JL]Ludong Univ, Sch Informat & Elect Engn, Yantai, Peoples R China;[Wang, JL]Shandong Univ, Sch Comp Sci & Technol, Jinan, Shandong, Peoples R 更多
来源:APPLIED INTELLIGENCE
出版年:2019
卷:49
期:9
页码:3159-3172
DOI:10.1007/s10489-019-01424-5
关键词:Stochastic nonlinear heterogeneous super-systems; Deep fusion;; Hardware-software energy-saving principles; Energy-aware cloud; scheduling; Distributed artificial intelligence
摘要:Green computing of stochastic nonlinear heterogeneous super-systems, represented by the cloud, is a new demand for sustainable human developments. However, the scheduling middleware is now in urgent need of a series of theoretical breakthroughs from homogeneity to heterogeneity, linearity to non-linearity, and even fuzzy decision-making to scientific decision-making based on mathematical model. Focusing on deep fusion of hardware-software energy-saving principles, an energy-aware intelligent scheduling model and algorithm are proposed in this paper; throughout the stages of model preparation, composition and algorithm designs, three features and innovations are included, which are formalizing hardware energy-saving principles via nonlinear regression quantization, a comprehensive evaluation model of adaptive green scheduling for stochastic nonlinear heterogeneous super-systems, and a scheduling algorithm with distributed evolutionary intelligence. Extensive simulator and simulation experiments highlight obvious superiorities in the proposed scheduler such as higher efficacy and better scalability, which fully considers nonlinear diversities of heterogeneous super-systems whether for data or computing intensive stochastic tasks.
收录类别:EI;SCOPUS;SCIE
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061977155&doi=10.1007%2fs10489-019-01424-5&partnerID=40&md5=54a9c32bf36fa9c000134e3d75894c29
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